Optimization methodology based on neural networks and genetic algorithms applied to electro-coagulation processes

• Authors: Ciprian Piuleac , Silvia Curteanu , Manuel Rodrigo , Cristina Sáez , Francisco Fernández
• Languages of publication: EN

Abstracts

EN

An optimization methodology based on neural networks and genetic algorithms was developed and used to optimize a real world process - an electro-coagulation process involving three pollutants at different concentrations: kaolin (250–1000 mg L−1), Eriochrome Black T solutions (50–200 mg L−1), and oil/water emulsion (1500–4500 mg L−1). Feed-forward neural networks using heterogeneous combination of transfer functions were developed, leading to good results in the validation stage (relative error about 8%). The parameters of the process (concentration of pollutant, time, pH0, conductivity and current density) were optimized handling the genetic algorithm parameters, in order to obtain a maximum removal efficiency for each pollutant. Therefore, the optimization methodology combines neural networks as modeling tools with genetic algorithms as solving method. Validation of the optimization results using supplementary experimental data reveals errors under 11%. [...]

Keywords

EN

Electro-coagulation; Genetic algorithms; Neural networks; Modeling; Optimization

• Journal: Open Chemistry
• Year: 2013
• Volume: 11
• Issue: 7
• Pages: 1213-1224

Dates

published

1 - 7 - 2013

online

26 - 4 - 2013

Contributors

author

Ciprian Piuleac

• Department of Chemical Engineering, “Gheorghe Asachi” Technical University of Iasi, 700050, Iasi, Romania

author

Silvia Curteanu

• Department of Chemical Engineering, “Gheorghe Asachi” Technical University of Iasi, 700050, Iasi, Romania,

author

Manuel Rodrigo

• Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004, Ciudad Real, Spain

author

Cristina Sáez

• Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004, Ciudad Real, Spain

author

Francisco Fernández

• Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004, Ciudad Real, Spain

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Identifiers

DOI

10.2478/s11532-013-0253-0